lbo-model

Solid

Build leveraged buyout models in Excel — sources & uses, debt schedule, cash sweep, exit multiple, IRR/MOIC sensitivity. Pairs with excel-author. Use for PE screening, sponsor-case valuation, or illustrative LBO in a pitch.

AI & Automation 173,893 stars 29465 forks Updated today MIT

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Skill Content

## Environment This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk. Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`. --- ## TEMPLATE REQUIREMENT **This skill uses templates for LBO models. Always check for an attached template file first.** Before starting any LBO model: 1. **If a template file is attached/provided**: Use that template's structure exactly - copy it and populate with the user's data 2. **If no template is attached**: Ask the user: *"Do you have a specific LBO template you'd like me to use? If not, I can use the standard template which includes Sources & Uses, Operating Model, Debt Schedule, and Returns Analysis."* 3. **If using the standard template**: Copy `examples/LBO_Model.xlsx` as your starting point and populate it with the user's assumptions **IMPORTANT**: When a file like `LBO_Model.xlsx` is attached, you MUST use it as your template - do not build from scratch. Even if the template seems complex or has more features than needed, copy it and adapt it to the user's requirements. Never decide to "build from scratch" when a template is provided. --- ## CRITICAL INSTRUCTIONS — READ FIRST Use Python/openpyxl. Write formula strings (`ws["D20"] = "=B5*B6"`), then run the `excel-author` skill's `recalc.py` helper before delivery. ### Core Principles * **Every cal...

Details

Author
NousResearch
Repository
NousResearch/hermes-agent
Created
10 months ago
Last Updated
today
Language
Python
License
MIT

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